Condense question prompt. CONDENSE_QUESTION_PROMPT = PromptTemplate.

Condense question prompt question_answering import load_qa_chain # Construct a You signed in with another tab or window. It is easy enough to use OpenAI’s embedding API to convert documents, or chunks of documents to embeddings. This parameter accepts a list of ChatMessage objects, each representing a message in the conversation history. from_template(_template) template = """You are an AI assistant for the open source library LangChain. The default prompt used in this chain is CONDENSE_QUESTION_PROMPT, which is used to condense the chat history and new question into a standalone question for the retrieval step. Based on my understanding, you are experiencing a "ModuleNotFoundError" when trying to import "CONDENSE_QUESTION_PROMPT" from the "langchain. The issue is that the memory is not working. LlamaIndex 🦙 0. Other options include the. For this, we’ll be using LangChain, Azure OpenAI Service, and Faiss as our vector store. chain_type: The chain type to use to create the combine_docs_chain, will be sent to `load_qa_chain`. It then queries the query engine with this condensed question to provide a response. LlamaIndex is a data framework for your LLM applications - run-llama/llama_index Streaming for Chat Engine - Condense Question Mode Streaming Completion Prompts Customization Chat Prompts Customization ChatGPT (chat_history_str) return self. app. Chat History: {chat_history} Follow Up Input: {question} Standalone question:`; const QA_PROMPT = `You are an AI assistant. 5-turbo (the “ChatGPT” model). prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT from langchain. \\n\\n Context: \\n {context} \\n Condense Question Chat Engine; Condense Plus Context Chat Engine; Query Bundle; Query Transform; Data Connectors. Condense Question Mode; The Condense Question mode generates a standalone question from the conversation context and the last message. Update: its working when i add "{context}" in the system template like this: """End every answer should end with " This is the according to 10th article". Here is a complete example that includes all the steps such as loading the vector store, retriever, and LLM, and then chaining it with ConversationBufferWindowMemory: Hi, @0ENZO, I'm helping the LangChain team manage their backlog and am marking this issue as stale. Hello, I set my chat engine is as below: chat_engine = index. 7B models are performant but they’re not perfect so providing a handful of examples in the prompt is a good idea. Updating Prompts#. from_template(""" Use the following pieces of context and chat history to answer the question at the end. verbose (bool) – Verbosity flag for logging to stdout. The approach taken by this application for follow-up questions is to use the LLM to create a condensed question that summarizes the entire conversation, to be used for the retrieval phase. The code: template2 = """ Your name is Bot. load_local("path_to_my_vector_DB", embeddings) memory = ConversationBufferMemory(memory_key="chat_history", output_key='answer', return_messages=True) CONDENSE_QUESTION_PROMPT = First condense a conversation and latest user message to a standalone question. prompts import CONDENSE_QUESTION_PROMPT. You signed in with another tab or window. If you want to change this prompt, If you stumbled upon this page while looking for ways to pass system message to a prompt passed to ConversationalRetrievalChain using ChatOpenAI, you can try wrapping SystemMessagePromptTemplate in a ChatPromptTemplate. If you don't know the answer, just say that you don't know, don't try to make up an answer. _condense_question_prompt, question = last_message, chat_history = chat_history_str, ) I'm considering whether it's better to condense the question only when chat_history is not empty, as it cloud reduce unnecessary interactions with the LLM. Hello @gich2009!It's great to see you back here again. Try Teams for free Explore Teams. chains import LLMChain condense_question_prompt = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. condense question chat engine (will query the index every time, but can lead to some "forced" logic/conversation) an agent with an index as a tool. from llama_index. generic_utils import messages_to_history_str from llama_index. have a look at this snipped from ConversationalRetrievalChain class. I will provide information based on the context given, without relying on prior knowledge. _condense_prompt_template, question = latest_message, chat_history = chat_history_str,) async def _acondense_question (self, chat_history: List [ChatMessage] condense_question_prompt – The prompt to use to condense the chat history and new question into a standalone question. from_template (_template) Modify the prompt as you see fit for your use case. ; There's no point in using Introduction #. Follow Up Input: {question} Standalone question:""" CONDENSE_QUESTION_PROMPT = PromptTemplate. base. Also, the template strings in condense_question_prompt = PromptTemplate. Here's how you can achieve this: To add a custom prompt, you can modify the ChatMessage objects in the TEXT_QA_PROMPT_TMPL_MSGS and CHAT_REFINE_PROMPT_TMPL_MSGS lists. from_template(_template) Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company ConversationalRetrievalChain uses condense_question_prompt to find the question. . Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. but while generating the response the llm is attaching the entire prompt and relevant document at the output. e. Its default prompt is CONDENSE_QUESTION_PROMPT. Reload to refresh your session. g. LLM Integrations # LlamaIndex supports 40+ LLM integrations, from proprietary model providers like OpenAI, Anthropic to open-source models/model providers like Mistral, Ollama, Replicate. from docs. """ def __init__ ( self, query_engine: BaseQueryEngine, Condense question is a simple chat mode built on top of a query engine over your data. You can change the main prompt in ConversationalRetrievalChain by passing it in via I'm trying to create a ConversationalRetrievalChain to answer based on a specific context provided by a pdf file. Redis Cloud Fully managed service integrated with Google Cloud, Azure, and AWS for production-ready apps. questions, context_str = context_str) return {"questions_this_excerpt_can_answer": questions. prompt import PromptTemplate # Template setup template = """ You are HR assistant to select best candidates based on the resume based on the user input. The generate_response method adds the user's message to their session and then generates a response Note that prompts are prefixed by their sub-modules as "namespaces". Redis Software Self-managed Chat Engine - Condense Question Mode Chat Engine - Condense Question Mode Table of contents Download Data Get started in 5 lines of code Streaming Support Completion Prompts Customization Chat Prompts Customization ChatGPT HuggingFace LLM - StableLM HuggingFace LLM - Camel-5b Azure OpenAI Data Connectors Data Fine-tuning an Adapter; Embedding Fine-tuning Guide; Router Fine-tuning; Embedding Fine-tuning Repo; Embedding Fine-tuning Blog; GPT-3. Prompt that does question answering over provided context. Toggle table of contents sidebar. chains import ConversationalRetrievalChain from langchain. I have searched both the documentation and discord for an answer. : ``` memory = ConversationBufferMemory( chat_memory=RedisChatMessageHistory( session_id=conversation_id, url=redis_url, key_prefix="your_redis_index_prefix" ), condense_question_prompt_template = """ Given a chat history and the latest user question which might reference context in the chat history, formulate a standalone question which can be understood without the chat history. Use the following pieces of context to answer the question at the end. You can assume the question is about Chat History: {chat_history} Follow Up Input: {question} Standalone question:""" CONDENSE_QUESTION_PROMPT = PromptTemplate. I wanted to let you know that we are marking this issue as stale. Provide a conversational answer. Chat History: {chat_history} Follow Up Input: {question} Standalone question: 🤖. To pass system instructions to the ConversationalRetrievalChain. from_llm function, since it takes care of rephrasing the user’s question based on the Chat context. , condense_question_prompt=PROMPT, verbose=False, return_source_documents=True, memory=chat_history, get_chat_history=lambda h Chat Prompts Customization Completion Prompts Customization Streaming Streaming for Chat Engine - Condense Question Mode Data Connectors Data Connectors Chroma Reader DashVector Reader Database Reader DeepLake Reader Discord Reader Docling Reader Faiss Reader Github Repo LlamaIndex supports LLM abstractions and simple-to-advanced prompt abstractions to make complex prompt workflows possible. I believe it should be something at the end, but I cannot figure out what. I am trying to create a chatbot using Llama index that only answers only within the knowledge base that i give it using llama 2-70b model the issue i am facing while trying to using context chat engine i am getting an error:- NotImplementedError: Messages passed in must be of odd length. Condenses chat history into a standalone question. You can use ConversationBufferMemory with chat_memory set to e. I tried a bunch of things, but I can't retrieve it. llm = llm or Settings. Hello, Thank you for reaching out and providing detailed information about your issue. Based on your question, it seems you want to add a custom prompt to the CondenseQuestionChatEngine and also retain the chat history. from_template(_template) template = """You are an helpful AI assistant, if someone says HI, hello or any other greeting, try to answer in polite and mannered way as a human would. As mentioned in @Rijoanul Hasan Shanto's answer, make sure to include {context} into a template string so that it's recognized The condense_question_prompt is a BasePromptTemplate instance that defines how to condense the chat history and new question into a standalone question. llms. If you do not provide a condense_question_prompt, the default will be CONDENSE_QUESTION_PROMPT. basic_chains import get_condense_question_chain, get_stuff_documents_chain def get_conversation_retriever_chain(pdf_id:str=None, k:int=10, memory=get_buffer_memory()): Source: Twilix History of Retrieval Augmentation. The correct method to use here would be chat_engine. This approach is simple, and works for questions directly related to the knowledge base and general interactions. SQLChatMessageHistory (or Redis like I am using). apredict (prompt, num_questions = self. io. Source code in llama-index-core/llama_index/core/chat_engine/condense_question. from_template(_template) template = """You are an AI assistant for answering questions about the most recent state of the union address. from_llm to the LCEL method (create_history_aware_retriever, create_stuff_documents_chain, and create_retrieval_chain), provided that the return_generated_question attribute is set to True. Chat prompt templates. Selector prompt templates. Question. Contribute to langchain-ai/langchain development by creating an account on GitHub. E. This change seems to be intended as in this PR. Should save_context be part of the chain? Or do I have to handle it using some callback? At the end of the example is answer_chain, where the last step is skipped. ; SimpleDirectoryReader will select the appropriate file reader based on the extensions of the files in that directory (. For each chat interaction: query the query engine with the condensed question for a response. Streaming for Chat Engine - Condense Question Mode Data Connectors Data Connectors Chroma Reader DashVector Reader Database Reader DeepLake Reader Discord Reader Docling Reader Anthropic Prompt Caching Anthropic Prompt Caching Table of contents How Prompt Caching works Setup API Keys Setup LLM Download Data Load Data The load_qa_chain with map_reduce as chain_type requires two prompts, question and a combine prompts. You switched accounts on another tab or window. Tutorial # Pass the follow-up question along with the chat history to the LLM, and parse the answer (standalone_question). I'm having trouble trying to export the source documents and score from this code. chain_type (str) – The chain type to use First, the prompt that condenses conversation history plus current user input (condense_question_prompt), and second, the prompt that instructs the Chain on how to return a final response to the user (which happens in the First generate a standalone question from conversation context and last message, then query the query engine for a response. chain_type (str) – The chain type to use to create the combine_docs_chain, will be sent to load_qa_chain. For each chat interaction: first generate a standalone question from conversation context and last message, then. Examples Agents Agents 💬🤖 How to Build a Chatbot Build your own OpenAI Agent OpenAI agent: specifying a forced function call Building a Custom Agent To store chat history with your query engine setup, you can use the ChatMemoryBuffer class to manage the chat history. could not understand the difference between support prompt and CONDENSE_QUESTION_PROMPT – NSVR. From what I understand, you encountered validation errors for the ConversationalRetrievalChain in the provided code, and CONDENSE_QUESTION_PROMPT = PromptTemplate. Partial Formatting 2. Hello @nelsoni-talentu!Great to see you again in the LangChain community. chat() is not supported for streaming i. \n\n<Chat const CONDENSE_PROMPT = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. The documentation is located at https://langchain. py condense_question_prompt (BasePromptTemplate) – The prompt to use to condense the chat history and new question into a standalone question. from_template(_template) template = """You are an AI assistant for the . \n\n<Chat History>\n{chatHistory}\n\n<Follow Up 🤖. chat_engine. Thank you so much! In this example, MyCustomPromptTemplate is your custom prompt template that controls the follow-up questions asked by the model. verbose: Verbosity flag for Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. from_llm function. async achat ( message : str , Currently, when using ConversationalRetrievalChain (with the from_llm () function), we have to run the input through a LLMChain with a default "condense_question_prompt" The QA_PROMPT is the same as in the first article, It sets the tone and purpose for the bot. Beta Was this translation helpful? Give feedback. llm import LLM Instruction: Refine the existing answer using the provided context to assist the user. Implementation Example: Chat History: {chat_history} Follow Up Input: {question} Standalone question:""" CONDENSE_QUESTION_PROMPT = PromptTemplate. You are given the following extracted parts of a long document # CONDENSE_QUESTION_PROMPT Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. QA over documents. const condenseQuestionTemplate = ` Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. Advantages of switching to the LCEL implementation are similar to the RetrievalQA migration guide:. Answer the question as precise as possible using t he provided context. regarding the example above, we might do the from langchain. stuff_prompt import create_stuff_prompt_selector STUFF_PROMPT_SELECTOR = create_stuff_prompt_selector(ui_input= ui_input) #adds the ui_input to the prompt stuff_prompt = hwchase17/condense-question-prompt. @mcmoochi the context chat engine CAN be restrained, you just have to write a system prompt that encourages that behaviour. I hope you're doing well! Based on the information you've provided and the similar issues I found in the LlamaIndex repository, it seems you're trying to pass a system prompt to a CondensePlusContextChatEngine. strip ()} . _condense_prompt_template, question = latest_message, chat_history = chat_history_str,) async def _acondense_question (self, chat_history: List [ChatMessage] A couple of things here. Commented Jan 19 at 8:41 @NSVR Reference the original question and you will find the full code This answer references the information provided in the question. _condense_prompt_template, question = latest_message, chat_history = chat_history_str,) async def _acondense_question (self, chat_history: List [ChatMessage] Streaming for Chat Engine - Condense Question Mode Streaming Completion Prompts Customization Chat Prompts Customization ChatGPT (chat_history_str) return self. Human: If you don't see answer in the context just Reply "Sorry , the answer is not in the context so I don't know". def get_new_prompt(): custom_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. Assistant: Understood. With thousands of questions in rotation, you can expect a diverse range of unique and engaging prompts each time you use the generator. document_loaders import TextLoader from langchain. StrOutputParser() ). Then build a context for the standalone question from a retriever, Then pass the context along with prompt and user message to LLM to generate a response. readthedocs. from_defaults(chat_history=chat_history, llm=llm) if system_prompt is not None: raise NotImplementedError Chat Engine - Condense Question Mode Chat Engine - Condense Question Mode Table of contents Download Data Get started in 5 lines of code Streaming Support Chat Engine Advanced Prompt Techniques (Variable Mappings, Functions) EmotionPrompt in RAG So it has two step, you used condense_question_prompt=CUSTOM_QUESTION_PROMPT That use in first step, you should use this arg for step two combine_docs_chain_kwargs={"prompt": your prompt}, Share. chat_engine import CondenseQuestionChatEngine custom_prompt = PromptTemplate Question 3. CONDENSE_QUESTION_PROMPT = PromptTemplate. from_template(_template) template = """You are an AI assistant for answering questions about economics for the H2 Economics A-Levels. configure the condense question prompt, initialize the conversation with some existing history, print verbose debug message. but the chat engine when trying to make sense using the chat history it completely goes of the grid and ask the wrong The ConversationBufferWindowMemory class in LangChain is used to maintain a buffer of the most recent messages in a conversation, which helps in keeping the context for the language model. as_retriever() , memory=memory Migrating from ConversationalRetrievalChain. 0. This mode is suitable for questions directly related to the knowledge base. Chat History: {chat_history} Follow Up Input Ask questions, find answers and from langchain. as_chat_engine(chat_mode=“condense_question”, verbose=True) On my local machine it doesn’t answer question not related to the uploaded file as expected, but after deployment, it answers everything. Chat History: {chat_history} Follow Up Input: {question} Standalone question:""" CONDENSE_QUESTION_PROMPT = PromptTemplate. {'k': 4}), condense_question_prompt = PROMPT, ) An example of `CONDENSE_QUESTION_PROMPT` can be as follows: CONDENSE_QUESTION_TEMPLATE = """\ Rephrase the follow-up question based on the chat history to make it standalone. 1 You must be logged in to vote. conversational_retrieval. streamlit. Toggle child pages in navigation. hwchase17/qa-prompt. # Condense Prompt condense_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. astream_chat() as they are exclusively supported for streaming. The CONDENSE_QUESTION_PROMPT is new here. Chat history: {chat_history} I tried condense_question_prompt as well, but it is not giving an answer Im expecting. Here's how you can integrate it into your existing setup: Initialize the ChatMemoryBuffer: Create an instance of ChatMemoryBuffer to store the chat history. Use LlamaIndex’s SimpleDirectoryReader to passLlamaIndex's the folder where you’ve stored your data (in this case, it’s called data and sits at the base level of your repository). To manage terms not mentioned in your retriever, you can use the combine_docs_chain_kwargs parameter when calling the ConversationalRetrievalChain. These embeddings can be stored in a vector database such as Chroma, Faiss or Lance. conversational_retrieval. ; Modify the RetrieverQueryEngine: Update the RetrieverQueryEngine to include methods for storing and Toggle Light / Dark / Auto color theme. from_template(_template) template = """Use the following pieces of context to answer the question at the end. Take a look at how we do this. TS documentation! 🎉 If you are looking for the old documentationcheck it here. All reactions. Follow answered Sep 15, 2023 at 13:17. Condense Question Chat Engine. The concept of retrieval augmentation in the context of language models was first introduced by Google, in their paper — REALM: Retrieval-Augmented Language Model Pre 🦜🔗 Build context-aware reasoning applications. from langchain. predict (self. from_llm( llm, retriever, condense_question_prompt=CUSTOM_QUESTION_PROMPT, memory=memory, return_source_documents=True ) query = "what are cars made of?" result = qa({"question": query}) and in result you will get your source documents along with the scores of similarity Condense question is a simple chat mode built on top of a query engine over your data. Once asked the question Some of the context is derived from the condense_question_prompt: BasePromptTemplate = CONDENSE_QUESTION_PROMPT. You can see this in the source code here. Welcome to the new LlamaIndex. First generate a standalone question from conversation context and last message, then query the query engine for a response. Hamed Parvaresh Streaming for Chat Engine - Condense Question Mode Data Connectors Data Connectors Chroma Reader DashVector Reader Database Reader DeepLake Reader Discord Reader Docling Reader Faiss Reader Github Optional [Union [str, PromptTemplate]] = None, condense_prompt: Optional Here you are setting condense_question_prompt which is used to generate a standalone question using previous conversation history. 322, the required input keys for the ConversationalRetrievalChain are CONDENSE_QUESTION_PROMPT = PromptTemplate. In LangChain version 0. The server creates a Pinecone index to store embeddings of the text documents and retrieves the most similar documents to the user's condensed question, along with the condensed question itself and the chat history (if possible and available). OpenAI function calling for Sub-Question Query Engine Param Optimizer Param Optimizer [WIP] Hyperparameter Optimization for RAG Prompts Prompts Advanced Prompt Techniques (Variable Mappings, Functions) EmotionPrompt in RAG Accessing/Customizing Prompts within I am using the ConversationalRetrievalChain to answer a question based on various documents. chains. invoke(user_question condense_chain = CONDENSE_QUESTION_PROMPT | llm_to_condense | StrOutputParser() As a result, the condensed query is more suitable for context retrieval: Define a function called load_data(), which will:. What you want to do is: qa = condense_question_prompt = condense_question_prompt or DEFAULT_PROMPT. Just pass in argument values with the keys equal to the keys you see in the prompt dictionary obtained through get_prompts. The user interacts through a “chat interface” and The prompt looks like this. I hope your project is going well. Community Edition In-memory database for caching and streaming. Hello, Based on the information you provided and the context from the LangChain repository, there are a couple of ways you can change the final prompt of the ConversationalRetrievalChain without modifying the LangChain source code. The continuous updates ensure you won’t see the same questions too frequently. stream_chat() or chat_engine. Once we’ve formulated the search query and retrieved relevant documents, Streaming for Chat Engine - Condense Question Mode Data Connectors Data Connectors Chroma Reader DashVector Reader Database Reader DeepLake Reader Discord Reader Docling Reader Advanced Prompt Techniques (Variable Mappings, Functions) EmotionPrompt in Take a few moments to consider the prompt and let your imagination explore possibilities. Variable: defaultCondenseQuestionPrompt. You can customize prompts on any module that implements get_prompts with the update_prompts function. from_defaults in your RAG agent implementation, you can use the chat_history parameter. What you want to do is: qa = ConversationalRetrievalChain. I'm Dosu, and I'm here to help the LangChain team manage their backlog. query the query engine with the condensed question for a response. This Condense Question Chat Engine. [ ] For each chat interaction: first generate a standalone question from conversation context and last message, then ; query the query engine with the condensed question for a Examples Agents Agents 💬🤖 How to Build a Chatbot Build your own OpenAI Agent OpenAI agent: specifying a forced function call Building a Custom Agent 🤖. We pass the documents through an “embedding model”. retriever = vectordb. The goal is to let the prompt guide your creativity while maintaining a natural flow. prompts. memory import ConversationBufferWindowMemory. llm. can anyone please tell me how can I remove the prompt and the Question section and get only the Answer in response ? Code: from langchain_community. You can also pass other optional parameters to further customize the chain. You are a from langchain. To pass previous responses and context to the CondenseQuestionChatEngine. it is a synchronous function that will return the response after being generated. 2 replies Comment options # custom imports from memory. I was trying to build a RAG LLM model using opensource models. Multi-Modal LLM using OpenAI GPT-4V model for image reasoning; Multi-Modal LLM using Google’s Gemini model for image understanding and build Retrieval Augmented Generation with LlamaIndex Ask questions, find answers and collaborate at work with Stack Overflow for Teams. In this post we discuss how we can build a system that allows you to chat with your private data, similar to ChatGPT. This approach is simple, and works for questions directly related to the OpenAI function calling for Sub-Question Query Engine Param Optimizer Param Optimizer [WIP] Hyperparameter Optimization for RAG Prompts Prompts Advanced Prompt Techniques (Variable Mappings, Functions) Advanced Prompt Techniques (Variable Mappings, Functions) Table of contents 1. I can get good answers. As the underlying Large Language Model, we’ll be using gpt-3. I thought i had solved it then i realize it was running on default llm which is openai, and after solving that issue all the prompts that i created for the Condensequestion started to fail, now if i run the question in query engine and print it i get the right answers. You can use the condense_question_prompt parameter while initializing the ConversationalRetrievalChain. The most I could do is to pass the my demand to the prompt so the LLM retrieves it to me, but sometimes it just ignores me or hallucinates (ex: it gives me a source link from inside the text). # Load from local storage embeddings = OpenAIEmbeddings() vectordb = FAISS. The question prompt is used to ask the LLM to answer a question based on the provided context. Examples Agents Agents 💬🤖 How to Build a Chatbot Build your own OpenAI Agent OpenAI agent: specifying a forced function call Building a Custom Agent Question Validation. Improve this answer. with_config( run_name="CondenseQuestion", ) retriever_chain = condense_question_chain | retriever. You can define prompt like this. as_retriever(search Condense question is a simple chat mode built on top of a query engine over your data. Default Prompts# Completion prompt templates. BasePydanticReader; BaseReader; We then show the base prompt template class and its subclasses. core import PromptTemplate from llama_index. chain_type – The chain type to use to create the combine_docs_chain, will be sent to load_qa_chain. 8. from pymilvus import connections, FieldSchema, CollectionSchema, DataType, Collection, utility In this example, UserSessionJinaChat is a subclass of JinaChat that maintains a dictionary of user sessions. Once you find a compelling prompt, set a timer for 10-15 minutes and start writing without self-editing. from_llm method in the LangChain framework, _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. How does it work with map_prompt and combine_prompt being same? Answer 3 The fact that both prompts are the same here looks like it may be for the convenience of the example, as the suggested prompt is generic: >`"Write a concise summary of the following: CONDENSE_QUESTION_PROMPT = PromptTemplate. from_template("""Given the following conversation and follow up question, rephrase follow up question to be a standalone question. Since I use large document parts, and to improve the quality of the answer, I first want to summarize each of the top-k retrieved documents based on the question posed, using a Hi, @fatjoni. Do NOT answer the question , just Streaming for Chat Engine - Condense Question Mode Streaming Completion Prompts Customization Chat Prompts Customization ChatGPT (chat_history_str) return self. can anyone please tell m I was trying to build a RAG LLM model using opensource models. core. chat_index" module in the Chat Engine - Condense Question Mode Chat Engine - Condense Question Mode Table of contents Download Data Get started in 5 lines of code Streaming Support Cookbooks Advanced Prompt Techniques (Variable Mappings, Functions) EmotionPrompt in RAG Prompt Engineering for RAG I am using from LLM and I don't want the reframed question to be returned along with the response hence I am using condense_question_llm, but how can I check the reframed question? I checked with return_generated_question = True, but version of the question using the LLMChain instance with the CONDENSE_PROMPT prompt. Hi Ken, Based on the information you've provided, it seems like you're trying to modify the prompt used in the ConversationalRetrievalChain. condense_question_prompt = PromptTemplate(input_variables= const defaultCondenseQuestionPrompt: PromptTemplate<readonly ["chatHistory", "question"], string[], "Given a conversation (between Human and Assistant) and a follow up message from Human, rewrite the message to be a standalone question that captures all relevant context from the conversation. 最初のQuestion generatorの部分に関しては、デフォルトではCONDENSE_QUESTION_PROMPTが使われますが、もちろん書き換えてもOKです。ちなみにCONDENSE_QUESTION_PROMPTを日本語で書くとこ In essence, the chatbot looks something like above. from_template(llmtemplate) def get_conversation_chain(vectordb, llm, memory): #retrieves top 2 serach results. # main. from_llm(). 🤖. py from langchain. At the First condense a conversation and latest user message to a standalone question Then build a context for the standalone question from a retriever, Then pass the context along with prompt and user message to LLM to generate a response. prompts import PromptTemplate # Adapt if needed CONDENSE_QUESTION_PROMPT = Streaming for Chat Engine - Condense Question Mode Data Connectors Data Connectors Chroma Reader DashVector Reader Database Reader DeepLake Reader Discord Reader Docling Reader LlamaIndex uses prompts to build the index, do insertion, perform traversal during querying, This setup includes a chat history and integrates the image data into the prompt, allowing you to send both text and images to the OpenAI GPT-4o model in a multimodal setup. 11 I have a simple RAG app and cannot figure out how to store memory with streaming. md files """ CONDENSE_PLUS_CONTEXT = "condense_plus_context" """Corresponds to `CondensePlusContextChatEngine`. The ConversationalRetrievalChain chain hides condense_question_prompt: The prompt to use to condense the chat history and new question into a standalone question. The ConversationalRetrievalChain was an all-in one way that combined retrieval-augmented generation with chat history, allowing you to "chat with" your documents. prompt_template = """ Human: You are a helpful, respectful, and honest assistant, dedicated to providing valuable and accurate information. I hope this helps! If you have any other questions, feel free to ask. If this is appropriate, I can submit a PR. To do this, you should use the context_prompt parameter 🤖. basic_memory import get_buffer_memory from vectorstore. fromTemplate To do this, we create a new LLMChain that will prompt our LLM with an instruction to condense our question. const defaultCondenseQuestionPrompt: PromptTemplate<readonly ["chatHistory", "question"], string[], "Given a conversation (between Human and Assistant) and a follow up message from Human, rewrite the message to be a standalone question that captures all relevant context from the conversation. This is the prompt that is used to generate a new, standalone question Here you are setting condense_question_prompt which is used to generate a standalone question using previous conversation history. It first combines the chat history (either explicitly passed in or retrieved from the provided memory) and the question into a standalone question, then looks up relevant documents from the retriever, and finally passes those Can you please post full code. verbose – Verbosity flag for logging to stdout. Each ChatMessage object has a role (either self. _llm. Streaming for Chat Engine - Condense Question Mode Data Connectors Data Connectors Chroma Reader DashVector Reader Database Reader DeepLake Reader Discord Reader questions = await self. Tbh the system prompt is powerful if written well. Clearer internals. Chat History: {chat_history} Follow Up Input: {question} Standalone question:""" CONDENSE_QUESTION_PROMPT = condense_question_prompt (BasePromptTemplate) – The prompt to use to condense the chat history and new question into a standalone question. Chat History: {chat_history} Yes, it is expected that the condensed_question being generated is also being returned when migrating code from using the legacy method ConversationalRetrievalChain. You signed out in another tab or window. document_loaders import chat_engine = CondenseQuestionChatEngine. {context}""" qa = ConversationalRetrievalChain. You are given the following extracted parts of a long document and a question. If the client sends a session_id argument in the query string of the request URL, then the question is assumed to be made in the context of any previous questions under that same session. In this case, the question_prompt is. Based on the information you've provided and the context from the LangChain repository, it seems like the issue is related to the input keys for the ConversationalRetrievalChain. Teams. llm. If the context isn't helpful, just repeat the existing answer and nothing more. First condense a conversation and latest user message to a standalone question. but when i use the condense question chat engine my code works fine its just that You can see the prompt we use for rephrasing a question here. from_defaults( query_engine=query_engine, condense_question_prompt=custom_prompt, chat_history=custom_chat_history, verbose=True ) This limitation is affecting my ability to have more complex interactions with the model, especially for conversational AI applications. I observe that in CondensePlusContextChatEngine, custom system_prompt is prepended to the default prompt instead of replacing as I would expect. e. from_llm( llm=llm, chain_type="stuff", retriever=doc_db. The documentation is located at . Chat History: {chat_history} Follow Up Input: {question} Standalone question: `; const CONDENSE_QUESTION_PROMPT = PromptTemplate. condense_question_prompt = PromptTemplate( template=condense_question_template, input_variables=["chat_history", "question"] ) 簡単に翻訳すると、「元の言語でユーザーの質問を会話履歴をもとに独立した質問に直しなさい。 {"context": retriever | format_docs, "question": RunnablePassthrough()} | CONDENSE_QUESTION_PROMPT | llm | StrOutputParser()) when I go to ask question the answer is coming like that: user_question ="In which month did the highest virtual card activation occur, and what could be the reasons for this?" result = rag_chain. 5 Fine-tuning Notebook (Colab) CondenseQuestionPrompt: PromptTemplate\\ Chat Prompts Customization Completion Prompts Customization Streaming Streaming for Chat Engine - Condense Question Mode Data Connectors Data Connectors Chroma Reader DashVector Reader Database Reader DeepLake Reader Discord Reader Docling Reader Faiss Reader Github Repo First condense a conversation and latest user message to a standalone question. This parameter is a dictionary of keyword Most platforms add new questions monthly, incorporating user suggestions and trending topics. Hi @Nat. context_prompt Streaming for Chat Engine - Condense Question Mode Data Connectors Data Connectors Chroma Reader DashVector Reader Database Reader DeepLake Reader Discord Reader Docling Reader Advanced Prompt Techniques (Variable Mappings, Functions) EmotionPrompt in Just answering my question, the difference between having chat_history in RetrievalQA is this in ConversationalRetrievalChain. My chatapp is at leonatezchat. llms import ChatMessage, MessageRole from llama_index. If the first prompt doesn’t inspire you, generate another one. load_vectors import get_vector_store from chains. Products. @classmethod def from_llm( cls, llm: BaseLanguageModel Chat History: {chat_history} Follow Up Input: {question} Standalone question:""" CONDENSE_QUESTION_PROMPT = PromptTemplate. chains. I’m almost sure it’s due to the condense_question_prompt parameter of langchain’s ConversationalRetrievalChain. chat_history = chat_history or [] memory = memory or memory_cls. grtug cvmx quarpi bdqikrg nmkclk rmjgi hfinzkh mbevwu aqgm plzgktn